Neural computing in cancer drug development: predicting mechanism of action.

Described here are neural networks capable of predicting a drug's mechanism of action from its pattern of activity against a panel of 60 malignant cell lines in the National Cancer Institute's drug screening program. Given six possible classes of mechanism, the network misses the correct category for only 12 out of 141 agents (8.5 percent), whereas linear discriminant analysis, a standard statistical technique, misses 20 out of 141 (14.2 percent). The success of the neural net indicates several things. (i) The cell line response patterns are rich in information about mechanism. (ii) Appropriately designed neural networks can make effective use of that information. (iii) Trained networks can be used to classify prospectively the more than 10,000 agents per year tested by the screening program. Related networks, in combination with classical statistical tools, will help in a variety of ways to move new anticancer agents through the pipeline from in vitro studies to clinical application.

An information-intensive approach to the molecular pharmacology of cancer.

Since 1990, the National Cancer Institute (NCI) has screened more than 60,000 compounds against a panel of 60 human cancer cell lines. The 50-percent growth-inhibitory concentration (GI50) for any single cell line is simply an index of cytotoxicity or cytostasis, but the patterns of 60 such GI50 values encode unexpectedly rich, detailed information on mechanisms of drug action and drug resistance. Each compound's pattern is like a fingerprint, essentially unique among the many billions of distinguishable possibilities. These activity patterns are being used in conjunction with molecular structural features of the tested agents to explore the NCI's database of more than 460,000 compounds, and they are providing insight into potential target molecules and modulators of activity in the 60 cell lines. For example, the information is being used to search for candidate anticancer drugs that are not dependent on intact p53 suppressor gene function for their activity. It remains to be seen how effective this information-intensive strategy will be at generating new clinically active agents.

Genomic-scale measurement of mRNA turnover and the mechanisms of action of the anti-cancer drug flavopiridol.

BACKGROUND: Flavopiridol, a flavonoid currently in cancer clinical trials, inhibits cyclin-dependent kinases (CDKs) by competitively blocking their ATP-binding pocket. However, the mechanism of action of flavopiridol as an anti-cancer agent has not been fully elucidated. RESULTS: Using DNA microarrays, we found that flavopiridol inhibited gene expression broadly, in contrast to two other CDK inhibitors, roscovitine and 9-nitropaullone. The gene expression profile of flavopiridol closely resembled the profiles of two transcription inhibitors, actinomycin D and 5,6-dichloro-1-beta-D-ribofuranosyl-benzimidazole (DRB), suggesting that flavopiridol inhibits transcription globally. We were therefore able to use flavopiridol to measure mRNA turnover rates comprehensively and we found that different functional classes of genes had distinct distributions of mRNA turnover rates. In particular, genes encoding apoptosis regulators frequently had very short half-lives, as did several genes encoding key cell-cycle regulators. Strikingly, genes that were transcriptionally inducible were disproportionately represented in the class of genes with rapid mRNA turnover. CONCLUSIONS: The present genomic-scale measurement of mRNA turnover uncovered a regulatory logic that links gene function with mRNA half-life. The observation that transcriptionally inducible genes often have short mRNA half-lives demonstrates that cells have a coordinated strategy to rapidly modulate the mRNA levels of these genes. In addition, the present results suggest that flavopiridol may be more effective against types of cancer that are highly dependent on genes with unstable mRNAs.